Fingerprinting positioning technologies, also known as radio pattern matching (RPM) or radio signal positioning (RSP), represent a family of path-loss based technologies that rely on matching the radio frequency (RF) environment, as experienced by the user equipment (UE), to the known, estimated or otherwise mapped characteristics of the larger RF system in which the UE is operating in order to generate position or location information for the UE. Information from the UE, including measurements of neighbor cell signal strengths, time delay and other network parameters form the basis of the RF environment that is compared to the established system RF database.
Thus, radio fingerprinting positioning methods are based on measurements made by the UE and the base station. One measurement set usable for radio fingerprinting is defined in 3GPP Technical Specification 25.215 and is incorporated herein by reference. Generally speaking, such fingerprinting positioning algorithms operate by creating a radio fingerprint for each point of a fine coordinate grid that overlays the radio access network (RAN). Each of the measurements associated with a radio fingerprinting positioning method can be associated with an identity of a radio base station (RBS), and possibly also one or more points of the fine coordinate grid.
The fingerprint position can, for example, comprise 1) the cell identities detected by the user equipment, in each grid point; 2) quantized path loss or signal strength measurements, with respect to multiple radio base stations, performed by the user equipment, in each grid point; 3) quantized round trip time (RTT) in Wideband Code Division Multiple Access (W-CDMA) networks, Timing Advance (TA) in Global System for Mobile (GSM) communications and Long Term Evolution (LTE) networks and UE receive-transmit time difference in LTE networks, in each grid point; 4) quantized noise rise, representing the load of a Code Division Multiple Access (CDMA) system, in each grid point; 5) quantized signal quality, e.g., RxQual parameter in GSM, Ec/N0 sensitivity parameter in W-CDMA and reference signal received quality (RSRQ) in LTE; 6) radio connection information, e.g., radio access bearer (RAB); and/or 7) quantized time. Whenever a position request arrives at a node which is responsible for providing position information, a radio fingerprint is measured, corresponding grid points with similar characteristics are looked up and a location estimate is calculated and reported.
Adaptive Enhanced Cell Identity (AECID) is a fingerprinting positioning technology, developed by Telefonaktiebolaget L M Ericsson (Publ), that refines the basic cell identity positioning method in a variety of ways. The AECID positioning method is based on the concept that high-precision positioning measurements, e.g., Assisted Global Positioning System (A-GPS) measurements, can be interpreted as points that belong to regions where a particular cellular radio propagation conditions persist.
In a first step of the AECID method, an A-GPS positioning is performed at the same time as a UE network signal measurement. The AECID positioning method introduces the concept of tagging of high-precision measurements according to the seven predefined criteria previously described above relative to a fingerprint. It is important to note that the tag consists of a vector of indices wherein each index comprises an enumerable number of discrete values. Accordingly, continuous value variables used for tagging, e.g., path loss, are quantized. As a second step of the AECID method, high-precision positioning measurements that have the same tag in different high-precision measurement clusters are collected and further processed.
A third step comprises computing a polygon, representing the geographical extension of a cluster, for each stored high-precision position measurement cluster. A geographical region in this context can be smaller than the extension of a cell of the cellular system.
A fourth and final step of the AECID fingerprinting algorithm comprises, for an incoming positioning request, obtaining the UE's network measurement, then, by looking up cell identities or tags, the polygon corresponding to the determined tag is looked up in a tagged database of polygons and reported, e.g., in the WCDMA system over the Radio Access Network Application Part (RANAP), using the polygon format. The two most pronounced properties of the AECID fingerprinting algorithm comprise minimizing the area of the polygon, i.e., accuracy is maximized, and precisely known, as a constraint in the algorithm, and determining the probability that the terminal is within the polygon, i.e., the confidence.
A significant parameter for any positioning algorithm is the horizontal accuracy or inaccuracy. Although simple in theory, this parameter is not straightforward to define, based on the 3GPP standard defining a plurality of position formats with many of the formats involving irregularly shaped areas. Some of these formats have explicit uncertainty information while others have the uncertainty information implicitly embedded in other aspects of the format.
One possible method of computing the inaccuracy of a positioning algorithm is to first compute the area of the region in which the UE is determined to reside. The accuracy is then computed as the radius of a circle having the same area as the region where the UE is located. A more conservative accuracy measurement is obtained if the inaccuracy is considered to be the maximum distance between a center point of the region where the UE is located, and any other point in the region. It should be noted that the accuracy is not the only parameter of interest, the confidence, defined as the probability that the UE is actually within the reported region, is of equal importance. The importance of the confidence resides in the balance struck between accuracy and confidence, i.e., a better accuracy can be stated when associated with a reduction in the confidence of the reported accuracy. Accordingly, both parameters are desirable when the associated standard allows.
Accuracy and confidence are useful in several parts of the positioning system. When a positioning request arrives, at the positioning node, from an end user, a decision must be made on which positioning algorithm to employ. The positioning node looks up prior accuracies of the available positioning methods and compares these to the signalled requested accuracy from the end user to determine which positioning method to select. Next, after receiving a positioning result from the selected positioning algorithm, the achieved accuracy is calculated and, if the requested accuracy was achieved, the positioning node reports the result and possibly the accuracy to the end user. If the requested accuracy is not met then the positioning node can select another positioning algorithm and attempt another positioning request.
Looking specifically to the AECID fingerprinting positioning method, accuracy is impacted by many factors. First, there is the accuracy of the A-GPS, Uplink Time Difference of Arrival (U-TDOA), and Observed Time Difference of Arrival (OTDOA) high-accuracy measurements, i.e., the reference positions that are clustered, to be considered. Outside metropolitan areas, A-GPS has an expected accuracy on the order of a few meters but within metropolitan areas, it has accuracy similar to U-TDOA and OTDOA, in the ten to one hundred meter range based on A-GPS coverage issues associated with being indoors or in the shadow of tall buildings. Second, there is the accuracy of the measurements used to create the fingerprint to be considered. In this context, TA and RTT are beneficial when compared to path loss and signal strength, that are known to have accuracies of about half the measured distance based on shadow fading.
Third, the radio environment itself is a factor in the accuracy of the AECID fingerprinting positioning algorithm's results. As one example, path loss measurements are sensitive to the propagation conditions associated with the radio environment. Fourth, the positioning traffic should also be considered. Densely populated areas will have a greater amount of positioning traffic than sparsely populated areas and consequently it will be more difficult to populate the positioning database in sparsely populated areas. Simulation software can be used to generate virtual data but the burden of accuracy is then shifted to the quality of the simulation software. Fifth, the cell planning in the target area also impacts accuracy of the AECID algorithm. Similar to the volume of traffic in urban versus rural areas, the number of cells in rural areas are smaller and separated by a greater distance than the cells in an urban area, leading to a reduction in resolution/discrimination among fingerprints and a lower level of accuracy.
In summary, the accuracy of fingerprinting methods in general, and AECID as a particular example, has a dependency on 1) the quality/quantity of the collected field measurements; 2) the environmental impact, e.g., fading due to mobility and different buildings; and 3) radio cell planning and distribution of RBSs. All of the above referenced factors can differ from location to location making it difficult to predict how accurate a positioning result will be before the positioning result has been produced. Consequently, the available methods for generating an a priori accuracy estimate suffer from poor performance.
Accordingly, since, for example, there are growing market segments for location-based services that require both location accuracy and other information associated with location accuracy, it would be desirable to provide devices, systems and methods for enabling estimation of the accuracy of positioning measurements and their associated algorithms in such systems that avoid the afore-described problems and drawbacks.